Converting speech in audio to raw text is done using an Automatic Speech Recognition (ASR) system. Speech recognition systems have drastically improved during recent years with advancements such as various data augmentation techniques [1]}, pre-training procedures on unlabelled speech data [2]} and noisy student training [3]}. State of the art performance on the common English benchmark dataset LibriSpeech [4]} is as low as 1.4% and 3.3% of Word Error Rate (WER) on the test-clean and the test-other partitions, respectively [5]}.
Labeled speech recognition training data is highly accessible in English [4]}, [7]} but less so in many other languages. Even with crowd-sourcing data initiatives such as CommonVoice from Mozilla [8]}, the recognition performance gap between low and high resource languages remains quite high.https://paperswithcode.com/dataset/common-voice Pre-training speech recognition models with cross-lingual data has helped bridge the gap significantly [9]}. However, transcripts from ASR systems for low resource languages are likely to be error-prone, especially for complex audio data such as podcasts.
